Results Template

A Subtitle

Introduction

Packages & Data

Packages

This document was prepared on 2021-11-03.

library(tidyverse)
library(patchwork)
library(glmmTMB)
library(report)
library(parameters)
library(modelbased)
library(performance)
# library(bayestestR)
# library(see)

summary(report::report(sessionInfo()))

The analysis was done using the R Statistical language (v4.1.0; R Core Team, 2021) on Windows 10 x64, using the packages ggplot2 (v3.3.5), stringr (v1.4.0), forcats (v0.5.1), tidyr (v1.1.3), readr (v1.4.0), dplyr (v1.0.6), tibble (v3.1.2), purrr (v0.3.4), parameters (v0.14.0.2), performance (v0.7.3.1), modelbased (v0.9.0), report (v0.3.5), glmmTMB (v1.1.2.3), patchwork (v1.1.1) and tidyverse (v1.3.1).

Data

setwd("C:/Users/user/Desktop/Sputnik/2019-23/DeceptionInteroTom")
df <- read.csv("data/data_combined.csv") %>% 
  mutate(ID = as.factor(ID),
         condition = as.factor(condition),
         item = as.factor(item),
         style = as.factor(style),
         instruction = as.factor(instruction))

cat(paste("The data consists of",
          report::report_participants(df,
                                      participants = "ID",
                                      sex = "Gender",
                                      age = "Age")))

The data consists of 30 participants (Mean age = 21.1, SD = 2.1, range: [18, 25]; 63.3% females)

Note that the chunks generating figures in the code below have some arguments specified in their header, such as fig.width and fig.height, which controls the figure size. These were filled with an eponym argument defined in utils/config.R. We also set the resolution, i.e., dpi, to a low value so that the resulting file is lighter. But don’t forget to crank this value up (to 300-600) to get nice-looking results.

Descriptive Stats

df.plot <- df %>% 
  group_by(ID, condition, instruction) %>% 
  summarise(DT_confidence = mean(DT_confidence, na.rm = TRUE),
            DT_RT = mean(DT_RT, na.rm = TRUE),
            yoni_total = mean(yoni_total, na.rm = TRUE),
            BES_total = mean(BES_total, na.rm = TRUE),
            HCT_confidence = mean(HCT_confidence, na.rm = TRUE),
            HCT_accuracy = mean(HCT_accuracy, na.rm = TRUE),
            HCT_awareness = mean(HCT_awareness, na.rm = TRUE),
            MAIA_total = mean(MAIA_total, na.rm = TRUE),
            lie_ability = mean(lie_ability, na.rm = TRUE),
            lie_frequency = mean(lie_frequency, na.rm = TRUE),
            lie_negativity = mean(lie_negativity, na.rm = TRUE),
            lie_contextuality = mean(lie_contextuality, na.rm = TRUE))

Questionnaires

p <- ggplot(df.plot, aes(yoni_total)) + geom_histogram()
q <- ggplot(df.plot, aes(BES_total)) + geom_histogram()
r <- ggplot(df.plot, aes(yoni_total, BES_total)) + geom_point()
s <- ggplot(df.plot, aes(HCT_accuracy)) + geom_histogram()
t <- ggplot(df.plot, aes(MAIA_total)) + geom_histogram()
u <- ggplot(df.plot, aes(HCT_accuracy, MAIA_total)) + geom_point()
(p + q + r)/(s + t + u) 

Deception Task

much higher confidence in truth no diff in reaction time in truth or lie not much diff for diff conditions

p <- ggplot(df, aes(DT_confidence)) + geom_density()
q <- ggplot(df, aes(DT_RT)) + geom_density()

r <- ggplot(df, aes(instruction, DT_confidence, fill = condition)) + geom_boxplot()
s <- ggplot(df, aes(instruction, DT_RT, fill = condition)) + geom_boxplot()

(p + q)/(r + s)

Questionnaire and Deception Task

interesting that the social tests are more correlated with polygraph and vice versa

Yoni and BES

both yoni and BES do not seem to have effect in social condition, higher yoni/BES increases confidence in truth and decreases confidences in lie

p <- ggplot(df.plot, aes(x = yoni_total, y = DT_confidence, colour = instruction)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  facet_wrap(~condition)

q <- ggplot(df.plot, aes(x = BES_total, y = DT_confidence, colour = instruction)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  facet_wrap(~condition)

p + q

HCT

same effect in both social and polygraph, higher HCT accuracy decreases confidence in truth and increases confidence in lie

ggplot(df.plot, aes(x = HCT_accuracy, y = DT_confidence, colour = instruction)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  facet_wrap(~condition)

MAIA

same effect for lie in both social and polygraph, higher MAIA increases confidence higher MAIA increases confidence in lie for social and not polygraph

ggplot(df.plot, aes(x = MAIA_total, y = DT_confidence, colour = instruction)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  facet_wrap(~condition)

Inferential Stats (Experimental)

plot_expt <- function(model, var){
  means <- estimate_means(model, at = c(var, "instruction"))
  ggplot(means, aes_string(x = "instruction", y = "Mean", colour = var)) + 
    geom_line(aes_string(group = var)) +
    geom_pointrange(aes(ymin = CI_low, ymax= CI_high))
}

The effect of confidence on RT

Confidence has a significant impact on RT. Increased confidence decreases RT.

model <- glmmTMB(DT_confidence ~ DT_RT + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.66	0.03	0.95	0.60	0.71	22.6	Inf	0.00	fixed
DT_RT	-0.01	0.01	0.95	-0.03	0.00	-2.8	Inf	0.01	fixed

viz_data <- estimate_relation(model)
ggplot(data = viz_data, aes(x = DT_RT, y = Predicted)) +
  geom_point(data = df, aes(x = DT_RT, y = DT_confidence, color = ID), show.legend = FALSE) +
  geom_line() +
  geom_ribbon(aes(ymin = CI_low, ymax = CI_high), alpha = 0.3)

Confidence

Interaction of condition and instruction

There is a significant interaction of condition and instruction on confidence. The increase in confidence from lie to truth is more in polygraph than social.

model <- glmmTMB(DT_confidence ~ condition*instruction + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.42	0.02	0.95	0.38	0.46	19.50	Inf	0.00	fixed
conditionSocial	0.01	0.02	0.95	-0.02	0.04	0.74	Inf	0.46	fixed
instructionTRUTH	0.36	0.02	0.95	0.33	0.40	22.04	Inf	0.00	fixed
conditionSocial:instructionTRUTH	-0.05	0.02	0.95	-0.10	-0.01	-2.32	Inf	0.02	fixed

plot_expt(model, "condition")

Interaction of style and instruction

There is no significant interaction of style and instruction on confidence.

model <- glmmTMB(DT_confidence ~ style*instruction + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.43	0.02	0.95	0.39	0.48	20.11	Inf	0.00	fixed
styleIndirect	-0.01	0.02	0.95	-0.05	0.02	-0.90	Inf	0.37	fixed
instructionTRUTH	0.34	0.02	0.95	0.30	0.37	20.27	Inf	0.00	fixed
styleIndirect:instructionTRUTH	0.00	0.02	0.95	-0.04	0.05	0.09	Inf	0.93	fixed

plot_expt(model, "style")

Reaction time

Interaction of condition and instruction

There is no significant interaction of condition and instruction on RT.

model <- glmmTMB(DT_RT ~ condition*instruction + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.33	0.21	0.95	3.91	4.74	20.53	Inf	0.00	fixed
conditionSocial	-0.41	0.07	0.95	-0.54	-0.28	-6.10	Inf	0.00	fixed
instructionTRUTH	0.08	0.07	0.95	-0.05	0.21	1.19	Inf	0.23	fixed
conditionSocial:instructionTRUTH	-0.02	0.10	0.95	-0.20	0.17	-0.19	Inf	0.85	fixed

plot_expt(model, "condition")

Interaction of style and instruction

There is no significant interaction of style and instruction on RT. Indirect style significantly increases RT.

model <- glmmTMB(DT_RT ~ style*instruction + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	3.96	0.21	0.95	3.54	4.37	18.77	Inf	0.00	fixed
styleIndirect	0.33	0.07	0.95	0.20	0.46	4.91	Inf	0.00	fixed
instructionTRUTH	0.02	0.07	0.95	-0.11	0.15	0.28	Inf	0.78	fixed
styleIndirect:instructionTRUTH	0.10	0.10	0.95	-0.08	0.29	1.09	Inf	0.27	fixed

plot_expt(model, "style")

Inferential Stats (Behavioural)

Effect on confidence and RT

plot_behv <- function(model, varx, vary) {
  viz_data <- estimate_relation(model, at = c("condition", varx, "instruction"))
  ggplot(data = viz_data, aes_string(x = varx, y = "Predicted")) +
    geom_point(data = df.plot, aes_string(y = vary, color = "condition"), show.legend = FALSE) +
    geom_line(aes(color = condition)) +
    geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = condition), alpha = 0.3) + 
    facet_wrap(~instruction)
}

Summary

summary <- data.frame("yoni", "decrease (trend)", "social", "increase (ns)", "ns")
names(summary) <- c("measure", "conf main effect (lie)", "conf interact", "RT main effect (lie)", "RT interact")
summary <- summary %>% 
  rbind(c("BES", "decrease (ns)", "social (trend)", "decrease (ns)", "polygraph")) %>% 
  rbind(c("HCT confidence", "increase", "polygraph", "decrease", "ns")) %>% 
  rbind(c("HCT accuracy", "increase", "polygraph", "decrease (ns)", "ns")) %>% 
  rbind(c("HCT awareness", "decrease", "social", "increase", "polygraph")) %>% 
  rbind(c("MAIA", "increase", "ns", "decrease", "social"))
summary

measure	conf main effect (lie)	conf interact	RT main effect (lie)	RT interact
yoni	decrease (trend)	social	increase (ns)	ns
BES	decrease (ns)	social (trend)	decrease (ns)	polygraph
HCT confidence	increase	polygraph	decrease	ns
HCT accuracy	increase	polygraph	decrease (ns)	ns
HCT awareness	decrease	social	increase	polygraph
MAIA	increase	ns	decrease	social

TOM ability

Yoni task

When instructed to lie, the decrease in confidence in participants with higher yoni score is less in social than polygraph condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*yoni_total) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.85	0.24	0.95	0.37	1.32	3.5	Inf	0.00	fixed
instructionTRUTH	-0.43	0.19	0.95	-0.80	-0.07	-2.3	Inf	0.02	fixed
instructionLIE:conditionSocial	-0.36	0.19	0.95	-0.73	0.01	-1.9	Inf	0.05	fixed
instructionTRUTH:conditionSocial	0.29	0.19	0.95	-0.07	0.66	1.6	Inf	0.11	fixed
instructionLIE:yoni_total	-0.01	0.00	0.95	-0.01	0.00	-1.8	Inf	0.08	fixed
instructionTRUTH:yoni_total	0.00	0.00	0.95	0.00	0.01	1.5	Inf	0.13	fixed
instructionLIE:conditionSocial:yoni_total	0.00	0.00	0.95	0.00	0.01	2.0	Inf	0.05	fixed
instructionTRUTH:conditionSocial:yoni_total	0.00	0.00	0.95	-0.01	0.00	-1.8	Inf	0.07	fixed

plot_behv(model, "yoni_total", "DT_confidence")

When instructed to lie, participants with higher yoni score show no significant difference in RT between social and polygraph condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*yoni_total) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	1.18	2.13	0.95	-3.00	5.36	0.55	Inf	0.58	fixed
instructionTRUTH	2.70	0.72	0.95	1.30	4.11	3.77	Inf	0.00	fixed
styleIndirect	0.40	0.05	0.95	0.30	0.49	8.46	Inf	0.00	fixed
instructionLIE:conditionSocial	0.70	0.72	0.95	-0.70	2.11	0.98	Inf	0.33	fixed
instructionTRUTH:conditionSocial	-1.85	0.72	0.95	-3.25	-0.44	-2.58	Inf	0.01	fixed
instructionLIE:yoni_total	0.03	0.02	0.95	-0.01	0.08	1.39	Inf	0.16	fixed
instructionTRUTH:yoni_total	0.00	0.02	0.95	-0.04	0.05	0.16	Inf	0.87	fixed
instructionLIE:conditionSocial:yoni_total	-0.01	0.01	0.95	-0.03	0.00	-1.56	Inf	0.12	fixed
instructionTRUTH:conditionSocial:yoni_total	0.02	0.01	0.95	0.00	0.03	1.95	Inf	0.05	fixed

plot_behv(model, "yoni_total", "DT_RT")

BES

When instructed to lie, the decrease in confidence in participants with higher BES score has a trend towards being less in social than polygraph condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*BES_total) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.70	0.19	0.95	0.32	1.08	3.64	Inf	0.00	fixed
instructionTRUTH	-0.15	0.15	0.95	-0.43	0.14	-0.99	Inf	0.32	fixed
instructionLIE:conditionSocial	-0.23	0.15	0.95	-0.52	0.06	-1.58	Inf	0.11	fixed
instructionTRUTH:conditionSocial	0.14	0.15	0.95	-0.14	0.43	0.98	Inf	0.33	fixed
instructionLIE:BES_total	0.00	0.00	0.95	-0.01	0.00	-1.47	Inf	0.14	fixed
instructionTRUTH:BES_total	0.00	0.00	0.95	0.00	0.01	1.18	Inf	0.24	fixed
instructionLIE:conditionSocial:BES_total	0.00	0.00	0.95	0.00	0.01	1.67	Inf	0.09	fixed
instructionTRUTH:conditionSocial:BES_total	0.00	0.00	0.95	-0.01	0.00	-1.27	Inf	0.20	fixed

plot_behv(model, "BES_total", "DT_confidence")

When instructed to lie, the decrease in RT in participants with higher BES score is more in social than polygraph condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*BES_total) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.53	1.76	0.95	1.07	7.99	2.57	Inf	0.01	fixed
instructionTRUTH	1.40	0.59	0.95	0.23	2.56	2.35	Inf	0.02	fixed
styleIndirect	0.40	0.05	0.95	0.31	0.49	8.59	Inf	0.00	fixed
instructionLIE:conditionSocial	1.71	0.59	0.95	0.55	2.88	2.88	Inf	0.00	fixed
instructionTRUTH:conditionSocial	0.04	0.59	0.95	-1.12	1.21	0.07	Inf	0.94	fixed
instructionLIE:BES_total	-0.01	0.02	0.95	-0.05	0.04	-0.23	Inf	0.82	fixed
instructionTRUTH:BES_total	-0.02	0.02	0.95	-0.07	0.02	-0.97	Inf	0.33	fixed
instructionLIE:conditionSocial:BES_total	-0.03	0.01	0.95	-0.04	-0.01	-3.60	Inf	0.00	fixed
instructionTRUTH:conditionSocial:BES_total	-0.01	0.01	0.95	-0.02	0.01	-0.84	Inf	0.40	fixed

plot_behv(model, "BES_total", "DT_RT")

Interoceptive ability

HCT confidence

When instructed to lie, the increase in confidence in participants with higher HCT confidence is more in polygraph than social condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*HCT_confidence) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.29	0.04	0.95	0.21	0.38	6.60	Inf	0.00	fixed
instructionTRUTH	0.48	0.04	0.95	0.41	0.55	13.47	Inf	0.00	fixed
instructionLIE:conditionSocial	0.09	0.04	0.95	0.02	0.16	2.42	Inf	0.02	fixed
instructionTRUTH:conditionSocial	-0.11	0.04	0.95	-0.18	-0.04	-3.14	Inf	0.00	fixed
instructionLIE:HCT_confidence	0.25	0.08	0.95	0.10	0.41	3.20	Inf	0.00	fixed
instructionTRUTH:HCT_confidence	0.02	0.08	0.95	-0.14	0.17	0.21	Inf	0.83	fixed
instructionLIE:conditionSocial:HCT_confidence	-0.15	0.06	0.95	-0.27	-0.02	-2.34	Inf	0.02	fixed
instructionTRUTH:conditionSocial:HCT_confidence	0.14	0.06	0.95	0.02	0.26	2.22	Inf	0.03	fixed

plot_behv(model, "HCT_confidence", "DT_confidence")

When instructed to lie, participants with higher HCT confidence show no significant difference in RT between social and polygraph condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*HCT_confidence) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	5.18	0.44	0.95	4.32	6.03	11.84	Inf	0.00	fixed
instructionTRUTH	0.09	0.15	0.95	-0.21	0.39	0.61	Inf	0.54	fixed
styleIndirect	0.40	0.05	0.95	0.30	0.49	8.17	Inf	0.00	fixed
instructionLIE:conditionSocial	-0.67	0.15	0.95	-0.97	-0.37	-4.39	Inf	0.00	fixed
instructionTRUTH:conditionSocial	-0.52	0.15	0.95	-0.82	-0.22	-3.43	Inf	0.00	fixed
instructionLIE:HCT_confidence	-1.79	0.75	0.95	-3.25	-0.33	-2.40	Inf	0.02	fixed
instructionTRUTH:HCT_confidence	-1.86	0.75	0.95	-3.32	-0.40	-2.49	Inf	0.01	fixed
instructionLIE:conditionSocial:HCT_confidence	0.34	0.26	0.95	-0.18	0.85	1.27	Inf	0.20	fixed
instructionTRUTH:conditionSocial:HCT_confidence	0.10	0.26	0.95	-0.41	0.62	0.38	Inf	0.70	fixed

plot_behv(model, "HCT_confidence", "DT_RT")

HCT accuracy

When instructed to lie, the increase in confidence in participants with higher HCT accuracy is more in polygraph than social condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*HCT_accuracy) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.22	0.07	0.95	0.08	0.35	3.2	Inf	0.00	fixed
instructionTRUTH	0.71	0.05	0.95	0.61	0.81	13.9	Inf	0.00	fixed
instructionLIE:conditionSocial	0.13	0.05	0.95	0.03	0.23	2.6	Inf	0.01	fixed
instructionTRUTH:conditionSocial	-0.09	0.05	0.95	-0.19	0.01	-1.8	Inf	0.06	fixed
instructionLIE:HCT_accuracy	0.34	0.10	0.95	0.13	0.54	3.2	Inf	0.00	fixed
instructionTRUTH:HCT_accuracy	-0.23	0.10	0.95	-0.43	-0.02	-2.2	Inf	0.03	fixed
instructionLIE:conditionSocial:HCT_accuracy	-0.20	0.08	0.95	-0.35	-0.04	-2.5	Inf	0.01	fixed
instructionTRUTH:conditionSocial:HCT_accuracy	0.09	0.08	0.95	-0.07	0.24	1.1	Inf	0.28	fixed

plot_behv(model, "HCT_accuracy", "DT_confidence")

When instructed to lie, participants with higher HCT accuracy show no significant difference in RT between social and polygraph condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*HCT_accuracy) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.64	0.67	0.95	3.33	5.95	6.95	Inf	0.00	fixed
instructionTRUTH	-0.22	0.22	0.95	-0.66	0.21	-1.00	Inf	0.32	fixed
styleIndirect	0.40	0.05	0.95	0.31	0.49	8.52	Inf	0.00	fixed
instructionLIE:conditionSocial	-0.41	0.22	0.95	-0.85	0.03	-1.85	Inf	0.06	fixed
instructionTRUTH:conditionSocial	-0.34	0.22	0.95	-0.78	0.09	-1.55	Inf	0.12	fixed
instructionLIE:HCT_accuracy	-0.82	1.02	0.95	-2.82	1.17	-0.81	Inf	0.42	fixed
instructionTRUTH:HCT_accuracy	-0.32	1.02	0.95	-2.32	1.68	-0.31	Inf	0.75	fixed
instructionLIE:conditionSocial:HCT_accuracy	0.00	0.34	0.95	-0.67	0.67	-0.01	Inf	0.99	fixed
instructionTRUTH:conditionSocial:HCT_accuracy	-0.18	0.34	0.95	-0.85	0.49	-0.52	Inf	0.60	fixed

plot_behv(model, "HCT_accuracy", "DT_RT")

HCT awareness

When instructed to lie, participants with higher HCT awareness have higher confidence in social than polygraph condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*HCT_awareness) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.42	0.02	0.95	0.37	0.46	19.27	Inf	0.00	fixed
instructionTRUTH	0.37	0.02	0.95	0.34	0.40	22.42	Inf	0.00	fixed
instructionLIE:conditionSocial	0.02	0.02	0.95	-0.01	0.05	1.13	Inf	0.26	fixed
instructionTRUTH:conditionSocial	-0.04	0.02	0.95	-0.07	-0.01	-2.52	Inf	0.01	fixed
instructionLIE:HCT_awareness	-0.09	0.04	0.95	-0.16	-0.02	-2.53	Inf	0.01	fixed
instructionTRUTH:HCT_awareness	0.01	0.04	0.95	-0.06	0.08	0.32	Inf	0.75	fixed
instructionLIE:conditionSocial:HCT_awareness	0.13	0.03	0.95	0.08	0.19	4.79	Inf	0.00	fixed
instructionTRUTH:conditionSocial:HCT_awareness	0.01	0.03	0.95	-0.04	0.07	0.43	Inf	0.67	fixed

plot_behv(model, "HCT_awareness", "DT_confidence")

When instructed to lie, the increase in RT in participants with higher HCT awareness is less in social than polygraph condition. (Strange considering confidence increases)

model <- glmmTMB(DT_RT ~ instruction/(condition*HCT_awareness) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.30	0.21	0.95	3.88	4.72	20.01	Inf	0.00	fixed
instructionTRUTH	0.05	0.07	0.95	-0.09	0.18	0.69	Inf	0.49	fixed
styleIndirect	0.40	0.05	0.95	0.30	0.49	8.17	Inf	0.00	fixed
instructionLIE:conditionSocial	-0.51	0.07	0.95	-0.64	-0.37	-7.38	Inf	0.00	fixed
instructionTRUTH:conditionSocial	-0.47	0.07	0.95	-0.61	-0.34	-6.84	Inf	0.00	fixed
instructionLIE:HCT_awareness	0.86	0.34	0.95	0.19	1.53	2.50	Inf	0.01	fixed
instructionTRUTH:HCT_awareness	0.64	0.34	0.95	-0.04	1.31	1.85	Inf	0.06	fixed
instructionLIE:conditionSocial:HCT_awareness	-0.31	0.12	0.95	-0.54	-0.08	-2.65	Inf	0.01	fixed
instructionTRUTH:conditionSocial:HCT_awareness	-0.08	0.12	0.95	-0.31	0.15	-0.66	Inf	0.51	fixed

plot_behv(model, "HCT_awareness", "DT_RT")

MAIA

When instructed to lie, participants with higher MAIA score show no significant difference in confidence between social and polygraph condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*MAIA_total) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.07	0.10	0.95	-0.13	0.28	0.71	Inf	0.48	fixed
instructionTRUTH	0.67	0.08	0.95	0.51	0.84	7.96	Inf	0.00	fixed
instructionLIE:conditionSocial	0.15	0.08	0.95	-0.02	0.32	1.76	Inf	0.08	fixed
instructionTRUTH:conditionSocial	-0.22	0.08	0.95	-0.39	-0.05	-2.60	Inf	0.01	fixed
instructionLIE:MAIA_total	0.13	0.04	0.95	0.05	0.20	3.41	Inf	0.00	fixed
instructionTRUTH:MAIA_total	0.01	0.04	0.95	-0.06	0.09	0.34	Inf	0.73	fixed
instructionLIE:conditionSocial:MAIA_total	-0.05	0.03	0.95	-0.11	0.01	-1.64	Inf	0.10	fixed
instructionTRUTH:conditionSocial:MAIA_total	0.07	0.03	0.95	0.01	0.13	2.15	Inf	0.03	fixed

plot_behv(model, "MAIA_total", "DT_confidence")

When instructed to lie, the decrease in RT in participants with higher MAIA score is more in polygraph than social condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*MAIA_total) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	6.61	0.98	0.95	4.69	8.54	6.73	Inf	0.00	fixed
instructionTRUTH	-0.03	0.35	0.95	-0.71	0.65	-0.09	Inf	0.93	fixed
styleIndirect	0.39	0.05	0.95	0.30	0.48	8.42	Inf	0.00	fixed
instructionLIE:conditionSocial	-1.63	0.35	0.95	-2.31	-0.96	-4.73	Inf	0.00	fixed
instructionTRUTH:conditionSocial	-1.40	0.35	0.95	-2.08	-0.73	-4.06	Inf	0.00	fixed
instructionLIE:MAIA_total	-0.90	0.35	0.95	-1.58	-0.22	-2.58	Inf	0.01	fixed
instructionTRUTH:MAIA_total	-0.86	0.35	0.95	-1.54	-0.17	-2.45	Inf	0.01	fixed
instructionLIE:conditionSocial:MAIA_total	0.44	0.12	0.95	0.20	0.68	3.60	Inf	0.00	fixed
instructionTRUTH:conditionSocial:MAIA_total	0.34	0.12	0.95	0.10	0.58	2.80	Inf	0.01	fixed

plot_behv(model, "MAIA_total", "DT_RT")

Lie scale

Lie ability

Higher lie ability increases confidence more in lie than truth. When instructed to lie, participants with higher lie ability show no significant difference in confidence between social and polygraph condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*lie_ability) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.43	0.02	0.95	0.39	0.47	21.30	Inf	0.00	fixed
instructionTRUTH	0.35	0.02	0.95	0.32	0.39	21.15	Inf	0.00	fixed
instructionLIE:conditionSocial	0.01	0.02	0.95	-0.02	0.04	0.60	Inf	0.55	fixed
instructionTRUTH:conditionSocial	-0.04	0.02	0.95	-0.07	-0.01	-2.36	Inf	0.02	fixed
instructionLIE:lie_ability	0.01	0.00	0.95	0.01	0.02	3.42	Inf	0.00	fixed
instructionTRUTH:lie_ability	0.00	0.00	0.95	-0.01	0.01	0.65	Inf	0.51	fixed
instructionLIE:conditionSocial:lie_ability	0.00	0.00	0.95	-0.01	0.00	-0.73	Inf	0.47	fixed
instructionTRUTH:conditionSocial:lie_ability	0.00	0.00	0.95	0.00	0.01	0.79	Inf	0.43	fixed

plot_behv(model, "lie_ability", "DT_confidence")

When instructed to lie, participants with higher lie ability show no significant difference in RT between social and polygraph condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*lie_ability) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.12	0.21	0.95	3.70	4.53	19.47	Inf	0.00	fixed
instructionTRUTH	0.09	0.07	0.95	-0.04	0.22	1.43	Inf	0.15	fixed
styleIndirect	0.40	0.05	0.95	0.30	0.49	8.44	Inf	0.00	fixed
instructionLIE:conditionSocial	-0.40	0.07	0.95	-0.53	-0.27	-6.10	Inf	0.00	fixed
instructionTRUTH:conditionSocial	-0.45	0.07	0.95	-0.58	-0.32	-6.72	Inf	0.00	fixed
instructionLIE:lie_ability	-0.04	0.04	0.95	-0.11	0.04	-0.95	Inf	0.34	fixed
instructionTRUTH:lie_ability	-0.03	0.04	0.95	-0.10	0.04	-0.79	Inf	0.43	fixed
instructionLIE:conditionSocial:lie_ability	0.02	0.01	0.95	-0.01	0.04	1.44	Inf	0.15	fixed
instructionTRUTH:conditionSocial:lie_ability	0.02	0.01	0.95	-0.01	0.04	1.37	Inf	0.17	fixed

plot_behv(model, "lie_ability", "DT_RT")

Lie frequency

When instructed to lie, participants with higher lie frequency show no significant difference in confidence between social and polygraph condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*lie_frequency) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.41	0.03	0.95	0.36	0.46	16.32	Inf	0.00	fixed
instructionTRUTH	0.38	0.02	0.95	0.35	0.42	19.85	Inf	0.00	fixed
instructionLIE:conditionSocial	0.01	0.02	0.95	-0.03	0.04	0.34	Inf	0.74	fixed
instructionTRUTH:conditionSocial	-0.04	0.02	0.95	-0.08	0.00	-2.00	Inf	0.05	fixed
instructionLIE:lie_frequency	0.00	0.00	0.95	-0.01	0.01	-0.47	Inf	0.64	fixed
instructionTRUTH:lie_frequency	0.01	0.00	0.95	0.00	0.01	1.11	Inf	0.27	fixed
instructionLIE:conditionSocial:lie_frequency	0.00	0.00	0.95	-0.01	0.01	-0.55	Inf	0.58	fixed
instructionTRUTH:conditionSocial:lie_frequency	0.00	0.00	0.95	-0.01	0.01	0.29	Inf	0.77	fixed

plot_behv(model, "lie_frequency", "DT_confidence")

Participants with higher lie frequency show significant increase in RT for both lie and truth. When instructed to lie, the increase in RT in participants with higher lie frequency is more in polygraph than social condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*lie_frequency) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.53	0.23	0.95	4.07	4.98	19.4	Inf	0.00	fixed
instructionTRUTH	0.19	0.08	0.95	0.04	0.34	2.5	Inf	0.01	fixed
styleIndirect	0.40	0.05	0.95	0.31	0.49	8.8	Inf	0.00	fixed
instructionLIE:conditionSocial	-0.77	0.08	0.95	-0.92	-0.62	-10.0	Inf	0.00	fixed
instructionTRUTH:conditionSocial	-0.84	0.08	0.95	-0.99	-0.69	-10.9	Inf	0.00	fixed
instructionLIE:lie_frequency	0.14	0.04	0.95	0.06	0.23	3.3	Inf	0.00	fixed
instructionTRUTH:lie_frequency	0.18	0.04	0.95	0.09	0.26	4.1	Inf	0.00	fixed
instructionLIE:conditionSocial:lie_frequency	-0.13	0.02	0.95	-0.16	-0.10	-8.4	Inf	0.00	fixed
instructionTRUTH:conditionSocial:lie_frequency	-0.14	0.02	0.95	-0.17	-0.11	-9.1	Inf	0.00	fixed

plot_behv(model, "lie_frequency", "DT_RT")

Lie negativity

When instructed to lie, participants with higher perception of lie negativity show no significant difference in confidence between social and polygraph condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*lie_negativity) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.40	0.03	0.95	0.34	0.47	12.17	Inf	0.00	fixed
instructionTRUTH	0.39	0.03	0.95	0.34	0.44	15.52	Inf	0.00	fixed
instructionLIE:conditionSocial	0.00	0.03	0.95	-0.05	0.05	0.16	Inf	0.88	fixed
instructionTRUTH:conditionSocial	-0.03	0.03	0.95	-0.08	0.02	-1.14	Inf	0.25	fixed
instructionLIE:lie_negativity	0.00	0.01	0.95	-0.01	0.02	0.69	Inf	0.49	fixed
instructionTRUTH:lie_negativity	0.00	0.01	0.95	-0.02	0.01	-0.47	Inf	0.64	fixed
instructionLIE:conditionSocial:lie_negativity	0.00	0.01	0.95	-0.01	0.01	0.43	Inf	0.66	fixed
instructionTRUTH:conditionSocial:lie_negativity	0.00	0.01	0.95	-0.01	0.01	-0.67	Inf	0.50	fixed

plot_behv(model, "lie_negativity", "DT_confidence")

Participants with higher perception of lie negativity show significant decrease in RT for both lie and truth. When instructed to lie, the decrease in RT in participants with higher perception of lie negativity is more in polygraph than social condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*lie_negativity) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.64	0.28	0.95	4.08	5.19	16.3	Inf	0.00	fixed
instructionTRUTH	0.11	0.09	0.95	-0.07	0.29	1.2	Inf	0.24	fixed
styleIndirect	0.40	0.05	0.95	0.31	0.49	8.7	Inf	0.00	fixed
instructionLIE:conditionSocial	-0.84	0.09	0.95	-1.02	-0.66	-9.0	Inf	0.00	fixed
instructionTRUTH:conditionSocial	-0.83	0.09	0.95	-1.01	-0.65	-8.8	Inf	0.00	fixed
instructionLIE:lie_negativity	-0.15	0.06	0.95	-0.26	-0.04	-2.6	Inf	0.01	fixed
instructionTRUTH:lie_negativity	-0.16	0.06	0.95	-0.27	-0.04	-2.7	Inf	0.01	fixed
instructionLIE:conditionSocial:lie_negativity	0.13	0.02	0.95	0.09	0.17	6.3	Inf	0.00	fixed
instructionTRUTH:conditionSocial:lie_negativity	0.11	0.02	0.95	0.07	0.15	5.6	Inf	0.00	fixed

plot_behv(model, "lie_negativity", "DT_RT")

Lie contextuality

When instructed to lie, participants with higher lie contextuality show no significant difference in confidence between social and polygraph condition.

model <- glmmTMB(DT_confidence ~ instruction/(condition*lie_contextuality) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.47	0.04	0.95	0.39	0.55	11.80	Inf	0.00	fixed
instructionTRUTH	0.32	0.03	0.95	0.26	0.38	10.25	Inf	0.00	fixed
instructionLIE:conditionSocial	0.00	0.03	0.95	-0.07	0.06	-0.12	Inf	0.90	fixed
instructionTRUTH:conditionSocial	0.01	0.03	0.95	-0.05	0.07	0.38	Inf	0.70	fixed
instructionLIE:lie_contextuality	-0.01	0.01	0.95	-0.03	0.00	-1.50	Inf	0.13	fixed
instructionTRUTH:lie_contextuality	0.00	0.01	0.95	-0.02	0.01	-0.24	Inf	0.81	fixed
instructionLIE:conditionSocial:lie_contextuality	0.00	0.01	0.95	-0.01	0.02	0.60	Inf	0.55	fixed
instructionTRUTH:conditionSocial:lie_contextuality	-0.01	0.01	0.95	-0.02	0.00	-2.03	Inf	0.04	fixed

plot_behv(model, "lie_contextuality", "DT_confidence")

When instructed to lie, the decrease in RT in participants with higher lie contextuality is more in social than polygraph condition.

model <- glmmTMB(DT_RT ~ instruction/(condition*lie_contextuality) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.04	0.39	0.95	3.28	4.80	10.45	Inf	0.00	fixed
instructionTRUTH	0.30	0.13	0.95	0.05	0.55	2.37	Inf	0.02	fixed
styleIndirect	0.41	0.05	0.95	0.32	0.50	8.76	Inf	0.00	fixed
instructionLIE:conditionSocial	0.21	0.13	0.95	-0.04	0.46	1.68	Inf	0.09	fixed
instructionTRUTH:conditionSocial	0.06	0.13	0.95	-0.18	0.31	0.51	Inf	0.61	fixed
instructionLIE:lie_contextuality	0.02	0.07	0.95	-0.12	0.15	0.25	Inf	0.80	fixed
instructionTRUTH:lie_contextuality	-0.03	0.07	0.95	-0.16	0.11	-0.39	Inf	0.70	fixed
instructionLIE:conditionSocial:lie_contextuality	-0.13	0.02	0.95	-0.18	-0.09	-5.75	Inf	0.00	fixed
instructionTRUTH:conditionSocial:lie_contextuality	-0.11	0.02	0.95	-0.16	-0.07	-4.77	Inf	0.00	fixed

plot_behv(model, "lie_contextuality", "DT_RT")

Full Code

The full script of executive code contained in this document is reproduced here.

# Set up the environment (or use local alternative `source("utils/config.R")`)
source("https://raw.githubusercontent.com/RealityBending/TemplateResults/main/utils/config.R")  

fast <- FALSE  # Make this false to skip the chunks
# This chunk is a bit complex so don't worry about it: it's made to add badges to the HTML versions
# NOTE: You have to replace the links accordingly to have working "buttons" on the HTML versions
if (!knitr::is_latex_output() && knitr::is_html_output()) {
  cat("![Build](https://github.com/RealityBending/TemplateResults/workflows/Build/badge.svg)
      [![Website](https://img.shields.io/badge/repo-Readme-2196F3)](https://github.com/RealityBending/TemplateResults)
      [![Website](https://img.shields.io/badge/visit-website-E91E63)](https://realitybending.github.io/TemplateResults/)
      [![Website](https://img.shields.io/badge/download-.docx-FF5722)](https://github.com/RealityBending/TemplateResults/raw/main/word_and_pdf/SupplementaryMaterials.docx)
      [![Website](https://img.shields.io/badge/see-.pdf-FF9800)](https://github.com/RealityBending/TemplateResults/blob/main/word_and_pdf/SupplementaryMaterials.pdf)")
}
library(tidyverse)
library(patchwork)
library(glmmTMB)
library(report)
library(parameters)
library(modelbased)
library(performance)
# library(bayestestR)
# library(see)

summary(report::report(sessionInfo()))
setwd("C:/Users/user/Desktop/Sputnik/2019-23/DeceptionInteroTom")
df <- read.csv("data/data_combined.csv") %>% 
  mutate(ID = as.factor(ID),
         condition = as.factor(condition),
         item = as.factor(item),
         style = as.factor(style),
         instruction = as.factor(instruction))

cat(paste("The data consists of",
          report::report_participants(df,
                                      participants = "ID",
                                      sex = "Gender",
                                      age = "Age")))
report::cite_packages(sessionInfo())
df.plot <- df %>% 
  group_by(ID, condition, instruction) %>% 
  summarise(DT_confidence = mean(DT_confidence, na.rm = TRUE),
            DT_RT = mean(DT_RT, na.rm = TRUE),
            yoni_total = mean(yoni_total, na.rm = TRUE),
            BES_total = mean(BES_total, na.rm = TRUE),
            HCT_confidence = mean(HCT_confidence, na.rm = TRUE),
            HCT_accuracy = mean(HCT_accuracy, na.rm = TRUE),
            HCT_awareness = mean(HCT_awareness, na.rm = TRUE),
            MAIA_total = mean(MAIA_total, na.rm = TRUE),
            lie_ability = mean(lie_ability, na.rm = TRUE),
            lie_frequency = mean(lie_frequency, na.rm = TRUE),
            lie_negativity = mean(lie_negativity, na.rm = TRUE),
            lie_contextuality = mean(lie_contextuality, na.rm = TRUE))
p <- ggplot(df.plot, aes(yoni_total)) + geom_histogram()
q <- ggplot(df.plot, aes(BES_total)) + geom_histogram()
r <- ggplot(df.plot, aes(yoni_total, BES_total)) + geom_point()
s <- ggplot(df.plot, aes(HCT_accuracy)) + geom_histogram()
t <- ggplot(df.plot, aes(MAIA_total)) + geom_histogram()
u <- ggplot(df.plot, aes(HCT_accuracy, MAIA_total)) + geom_point()
(p + q + r)/(s + t + u) 
p <- ggplot(df, aes(DT_confidence)) + geom_density()
q <- ggplot(df, aes(DT_RT)) + geom_density()

r <- ggplot(df, aes(instruction, DT_confidence, fill = condition)) + geom_boxplot()
s <- ggplot(df, aes(instruction, DT_RT, fill = condition)) + geom_boxplot()

(p + q)/(r + s)
p <- ggplot(df.plot, aes(x = yoni_total, y = DT_confidence, colour = instruction)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  facet_wrap(~condition)

q <- ggplot(df.plot, aes(x = BES_total, y = DT_confidence, colour = instruction)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  facet_wrap(~condition)

p + q
ggplot(df.plot, aes(x = HCT_accuracy, y = DT_confidence, colour = instruction)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  facet_wrap(~condition)
ggplot(df.plot, aes(x = MAIA_total, y = DT_confidence, colour = instruction)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  facet_wrap(~condition)
plot_expt <- function(model, var){
  means <- estimate_means(model, at = c(var, "instruction"))
  ggplot(means, aes_string(x = "instruction", y = "Mean", colour = var)) + 
    geom_line(aes_string(group = var)) +
    geom_pointrange(aes(ymin = CI_low, ymax= CI_high))
}
model <- glmmTMB(DT_confidence ~ DT_RT + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
viz_data <- estimate_relation(model)
ggplot(data = viz_data, aes(x = DT_RT, y = Predicted)) +
  geom_point(data = df, aes(x = DT_RT, y = DT_confidence, color = ID), show.legend = FALSE) +
  geom_line() +
  geom_ribbon(aes(ymin = CI_low, ymax = CI_high), alpha = 0.3)
model <- glmmTMB(DT_confidence ~ condition*instruction + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_expt(model, "condition")
model <- glmmTMB(DT_confidence ~ style*instruction + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_expt(model, "style")
model <- glmmTMB(DT_RT ~ condition*instruction + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_expt(model, "condition")
model <- glmmTMB(DT_RT ~ style*instruction + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_expt(model, "style")
plot_behv <- function(model, varx, vary) {
  viz_data <- estimate_relation(model, at = c("condition", varx, "instruction"))
  ggplot(data = viz_data, aes_string(x = varx, y = "Predicted")) +
    geom_point(data = df.plot, aes_string(y = vary, color = "condition"), show.legend = FALSE) +
    geom_line(aes(color = condition)) +
    geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = condition), alpha = 0.3) + 
    facet_wrap(~instruction)
}
summary <- data.frame("yoni", "decrease (trend)", "social", "increase (ns)", "ns")
names(summary) <- c("measure", "conf main effect (lie)", "conf interact", "RT main effect (lie)", "RT interact")
summary <- summary %>% 
  rbind(c("BES", "decrease (ns)", "social (trend)", "decrease (ns)", "polygraph")) %>% 
  rbind(c("HCT confidence", "increase", "polygraph", "decrease", "ns")) %>% 
  rbind(c("HCT accuracy", "increase", "polygraph", "decrease (ns)", "ns")) %>% 
  rbind(c("HCT awareness", "decrease", "social", "increase", "polygraph")) %>% 
  rbind(c("MAIA", "increase", "ns", "decrease", "social"))
summary
model <- glmmTMB(DT_confidence ~ instruction/(condition*yoni_total) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "yoni_total", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*yoni_total) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "yoni_total", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*BES_total) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "BES_total", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*BES_total) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "BES_total", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*HCT_confidence) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "HCT_confidence", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*HCT_confidence) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "HCT_confidence", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*HCT_accuracy) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "HCT_accuracy", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*HCT_accuracy) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "HCT_accuracy", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*HCT_awareness) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "HCT_awareness", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*HCT_awareness) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "HCT_awareness", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*MAIA_total) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "MAIA_total", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*MAIA_total) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "MAIA_total", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*lie_ability) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "lie_ability", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*lie_ability) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "lie_ability", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*lie_frequency) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "lie_frequency", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*lie_frequency) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "lie_frequency", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*lie_negativity) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "lie_negativity", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*lie_negativity) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "lie_negativity", "DT_RT")
model <- glmmTMB(DT_confidence ~ instruction/(condition*lie_contextuality) + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "lie_contextuality", "DT_confidence")
model <- glmmTMB(DT_RT ~ instruction/(condition*lie_contextuality) + style + (1|ID) + (1|item), data = df)
parameters(model, effects = "fixed")
plot_behv(model, "lie_contextuality", "DT_RT")

Package References

report::cite_packages(sessionInfo())

• H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
• Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr
• Hadley Wickham (2021). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.5.1. https://CRAN.R-project.org/package=forcats
• Hadley Wickham (2021). tidyr: Tidy Messy Data. R package version 1.1.3. https://CRAN.R-project.org/package=tidyr
• Hadley Wickham and Jim Hester (2020). readr: Read Rectangular Text Data. R package version 1.4.0. https://CRAN.R-project.org/package=readr
• Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.6. https://CRAN.R-project.org/package=dplyr
• Kirill Müller and Hadley Wickham (2021). tibble: Simple Data Frames. R package version 3.1.2. https://CRAN.R-project.org/package=tibble
• Lionel Henry and Hadley Wickham (2020). purrr: Functional Programming Tools. R package version 0.3.4. https://CRAN.R-project.org/package=purrr
• Lüdecke D, Ben-Shachar M, Patil I, Makowski D (2020). “Extracting,Computing and Exploring the Parameters of Statistical Models using R.”Journal of Open Source Software, 5(53), 2445. doi:10.21105/joss.02445 (URL: https://doi.org/10.21105/joss.02445).
• Lüdecke et al., (2021). performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139
• Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020). Estimation of Model-Based Predictions, Contrasts and Means. CRAN.
• Makowski, D., Ben-Shachar, M.S., Patil, I. & Lüdecke, D. (2020). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN. Available from https://github.com/easystats/report. doi: .
• Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Maechler and Benjamin M. Bolker (2017). glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2), 378-400.
• R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
• Thomas Lin Pedersen (2020). patchwork: The Composer of Plots. R package version 1.1.1. https://CRAN.R-project.org/package=patchwork
• Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686

References